pytd
pytd provides user-friendly interfaces to Treasure Data’s REST APIs, Presto query engine, and Plazma primary storage.
The seamless connection allows your Python code to efficiently read/write a large volume of data from/to Treasure Data. Eventually, pytd makes your day-to-day data analytics work more productive.
Installation
pip install pytd
Usage
Set your API
key
and
endpoint
to the environment variables, TD_API_KEY and TD_API_SERVER,
respectively, and create a client instance:
import pytd
client = pytd.Client(database='sample_datasets')
# or, hard-code your API key, endpoint, and/or query engine:
# >>> pytd.Client(apikey='1/XXX', endpoint='https://api.treasuredata.com/', database='sample_datasets', default_engine='presto')
Query in Treasure Data
Issue Presto query and retrieve the result:
client.query('select symbol, count(1) as cnt from nasdaq group by 1 order by 1')
# {'columns': ['symbol', 'cnt'], 'data': [['AAIT', 590], ['AAL', 82], ['AAME', 9252], ..., ['ZUMZ', 2364]]}
In case of Hive:
client.query('select hivemall_version()', engine='hive')
# {'columns': ['_c0'], 'data': [['0.6.0-SNAPSHOT-201901-r01']]} (as of Feb, 2019)
It is also possible to explicitly initialize pytd.Client for Hive:
client_hive = pytd.Client(database='sample_datasets', default_engine='hive')
client_hive.query('select hivemall_version()')
Here is an example of generator-based iterative retrieval using DB-API. For details, please refer to Documentation
from pytd.dbapi import connect
conn = connect(pytd.Client(database='sample_datasets'))
# or, connect with Hive:
# >>> conn = connect(pytd.Client(database='sample_datasets', default_engine='hive'))
def iterrows(sql, connection):
cur = connection.cursor()
cur.execute(sql)
index = 0
columns = None
while True:
row = cur.fetchone()
if row is None:
break
if columns is None:
columns = [desc[0] for desc in cur.description]
yield index, dict(zip(columns, row))
index += 1
for index, row in iterrows('select symbol, count(1) as cnt from nasdaq group by 1 order by 1', conn):
print(index, row)
When you face unexpected timeout error with Presto, you can try iterative way to retrieve data.
Write data to Treasure Data
Data represented as pandas.DataFrame can be written to Treasure Data
as follows:
import pandas as pd
df = pd.DataFrame(data={'col1': [1, 2], 'col2': [3, 10]})
client.load_table_from_dataframe(df, 'takuti.foo', writer='bulk_import', if_exists='overwrite')
For the writer option, pytd supports three different ways to ingest
data to Treasure Data:
Bulk Import API:
bulk_import(default)Convert data into a CSV file and upload in the batch fashion.
Presto INSERT INTO query:
insert_intoInsert every single row in
DataFrameby issuing an INSERT INTO query through the Presto query engine.Recommended only for a small volume of data.
td-spark:
sparkLocal customized Spark instance directly writes
DataFrameto Treasure Data’s primary storage system.
Characteristics of each of these methods can be summarized as follows:
|
|
|
|
|---|---|---|---|
Scalable against data volume |
✓ |
✓ |
|
Write performance for larger data |
✓ |
||
Memory efficient |
✓ |
✓ |
|
Disk efficient |
✓ |
||
Minimal package dependency |
✓ |
✓ |
Enabling Spark Writer
Since td-spark gives special access to the main storage system via PySpark, follow the instructions below:
Contact support@treasuredata.com to activate the permission to your Treasure Data account. Note that the underlying component, Plazma Public API, limits its free tier at 100GB Read and 100TB Write.
Install pytd with
[spark]option if you use the third option:pip install pytd[spark]
If you want to use existing td-spark JAR file, creating SparkWriter
with td_spark_path option would be helpful.
from pytd.writer import SparkWriter
writer = SparkWriter(td_spark_path='/path/to/td-spark-assembly.jar')
client.load_table_from_dataframe(df, 'mydb.bar', writer=writer, if_exists='overwrite')
Comparison between pytd, td-client-python, and pandas-td
Treasure Data offers three different Python clients on GitHub, and the following list summarizes their characteristics.
-
Basic REST API wrapper.
Similar functionalities to td-client-{ruby, java, node, go}.
The capability is limited by what Treasure Data REST API can do.
pytd
Access to Plazma via td-spark as introduced above.
Efficient connection to Presto based on presto-python-client.
Multiple data ingestion methods and a variety of utility functions.
pandas-td (deprecated)
Old tool optimized for pandas and Jupyter Notebook.
pytd offers its compatible function set (see below for the detail).
An optimal choice of package depends on your specific use case, but common guidelines can be listed as follows:
Use td-client-python if you want to execute basic CRUD operations from Python applications.
Use pytd for (1) analytical purpose relying on pandas and Jupyter Notebook, and (2) achieving more efficient data access at ease.
Do not use pandas-td. If you are using pandas-td, replace the code with pytd based on the following guidance as soon as possible.
How to replace pandas-td
pytd offers pandas-td-compatible functions that provide the same functionalities more efficiently. If you are still using pandas-td, we recommend you to switch to pytd as follows.
First, install the package from PyPI:
pip install pytd
# or, `pip install pytd[spark]` if you wish to use `to_td`
Next, make the following modifications on the import statements.
Before:
import pandas_td as td
In [1]: %%load_ext pandas_td.ipython
After:
import pytd.pandas_td as td
In [1]: %%load_ext pytd.pandas_td.ipython
Consequently, all pandas_td code should keep running correctly with
pytd. Report an issue from
here if you
noticed any incompatible behaviors.
Note
There is a known difference to pandas_td.to_td function for type conversion.
Since pytd.writer.BulkImportWriter, default writer pytd, uses CSV as an intermediate file before
uploading a table, column type may change via pandas.read_csv. To respect column type as much as possible,
you need to pass fmt=”msgpack” argument to to_td function.
For more detail, see fmt option of pytd.pandas_td.to_td().